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The deep hip muscles are often omitted in studies investigating hip contact forces using neuromusculoskeletal modelling methods. However, recent evidence indicates the deep hip muscles have potential to change the direction of hip contact force and could have relevance for hip contact loading estimates. Further, it is not known whether deep hip muscle excitation patterns can be accurately estimated using neuromusculoskeletal modelling or require measurement (through invasive and time-consuming methods) to inform models used to estimate hip contact forces. We calculated hip contact forces during walking, squatting, and squat-jumping for 17 participants using electromyography (EMG)-informed neuromusculoskeletal modelling with (informed) and without (synthesized) intramuscular EMG for the deep hip muscles (piriformis, obturator internus, quadratus femoris). Hip contact force magnitude and direction, calculated as the angle between hip contact force and vector from femoral head to acetabular center, were compared between configurations using a paired t-test deployed through statistical parametric mapping (P 0.05). Additionally, root mean square error, correlation coefficients (R

Achilles tendon material properties and geometry are altered in Achilles tendinopathy. The purpose of this study was to determine the relative contributions of altered material properties and geometry to free Achilles tendon stress distribution during a sub-maximal contraction in tendinopathic relative to healthy tendons. Tendinopathic (n = 8) and healthy tendons (n = 8) were imaged at rest and during a sub-maximal voluntary isometric contraction using three-dimensional freehand ultrasound. Images were manually segmented and used to create subject-specific finite element models. The resting cross-sectional area of the free tendon was on average 31% greater for the tendinopathic compared to healthy tendons. Material properties for each tendon were determined using a numerical parameter optimisation approach that minimised the difference in experimentally measured longitudinal strain and the strain predicted by the finite element model under submaximal loading conditions for each tendon. The mean Young’s modulus for tendinopathic tendons was 53% lower than the corresponding control value. Finite element analyses revealed that tendinopathic tendons experience 24% less stress under the same submaximal external loading conditions compared to healthy tendons. The lower tendon stress in tendinopathy was due to a greater influence of tendon cross-sectional area, which alone reduced tendon stress by 30%, compared to a lower Young’s modulus, which alone increased tendon stress by 8%. These findings suggest that the greater tendon cross-sectional area observed in tendinopathy compensates for the substantially lower Young’s modulus, thereby protecting pathological tendon against excessive stress.

Abnormal hip joint contact forces (HJCF) are considered a primary mechanical contributor to the progression of hip osteoarthritis (OA). Compared to healthy controls, people with hip OA often present with altered muscle activation patterns and greater muscle co-contraction, both of which can influence HJCF. Neuromusculoskeletal (NMS) modelling is non-invasive approach to estimating HJCF, whereby different neural control solutions can be used to estimate muscle forces. Static optimisation, available within the popular NMS modelling software OpenSim, is a commonly used neural control solution, but may not account for an individual's unique muscle activation patterns and/or co-contraction that are often evident in pathological population. Alternatively, electromyography (EMG)-assisted neural control solutions, available within CEINMS software, have been shown to account for individual activation patterns in healthy people. Nonetheless, their application in people with hip OA, with conceivably greater levels of co-contraction, is yet to be explored. The aim of this study was to compare HJCF estimations using static optimisation (in OpenSim) and EMG-assisted (in CEINMS) neural control solutions during walking in people with hip OA. EMG-assisted neural control solution was more consistent with both EMG and joint moment data than static optimisation, and also predicted significantly higher HJCF peaks (p 0.001). The EMG-assisted neural control solution also accounted for more muscle co-contraction than static optimisation (p = 0.03), which probably contributed to these higher HJCF peaks. Findings suggest that the EMG-assisted neural control solution may estimate more physiologically plausible HJCF than static optimisation in a population with high levels of co-contraction, such as hip OA.

A better understanding of deep hip muscle function is needed to establish whether retraining and strengthening these muscles is a worthwhile target for rehabilitation. This study aimed to determine the contribution of the deep hip muscles to the direction of hip loading in the acetabulum. Hip contact forces were calculated during walking and squatting for 12 participants (age: 24 ± 4 yrs, 4 females) using electromyography-informed neuromusculoskeletal modelling. Models were configured with different deep hip muscle activation levels: deep hip muscles (piriformis, obturator internus and externus, gemellus superior and inferior, and quadratus femoris) informed by intramuscular electromyography measurements (i.e., normal activation; assisted activation) and simulated with zero (no activation) or maximal (maximal activation) activation. The angle between the hip contact force and the vector from the femoral head to the acetabular center (hip contact force angle) was calculated for all configurations, where lower angles equated to hip loading directed towards the acetabular center. The position and spread of acetabular loading during both tasks were calculated for all configurations and compared using a within-participant analysis of variance via statistical parametric mapping (P 0.05). Maximal activation resulted in lower hip contact force angles and more anterior-inferior oriented, albeit a slightly reduced, spread of acetabular loading compared to assisted activation and no activation. Results suggest that, if activated maximally, the deep hip muscles can change the direction of hip loading away from commonly damaged areas of acetabular cartilage. Targeted training of these muscles may be relevant for individuals with hip pathology who present with unfavorable regional loading and/or cartilage lesions.

In-vivo hip joint contact forces (HJCF) can be estimated using computational neuromusculoskeletal (NMS) modelling. However, different neural solutions can result in different HJCF estimations. NMS model predictions are also influenced by the selection of neuromuscular parameters, which are either based on cadaveric data or calibrated to the individual. To date, the best combination of neural solution and parameter calibration to obtain plausible estimations of HJCF have not been identified. The aim of this study was to determine the effect of three electromyography (EMG)-informed neural solution modes (EMG-driven, EMG-hybrid, and EMG-assisted) and static optimisation, each using three different parameter calibrations (uncalibrated, minimise joint moments error, and minimise joint moments error and peak HJCF), on the estimation of HJCF in a healthy population (n = 23) during walking. When compared to existing in-vivo data, the EMG-assisted mode and static optimisation produced the most physiologically plausible HJCF when using a NMS model calibrated to minimise joint moments error and peak HJCF. EMG-assisted mode produced first and second peaks of 3.55 times body weight (BW) and 3.97 BW during walking; static optimisation produced 3.75 BW and 4.19 BW, respectively. However, compared to static optimisation, EMG-assisted mode generated muscle excitations closer to recorded EMG signals (average across hip muscles R2 = 0.60 ± 0.37 versus R2 = 0.12 ± 0.14). Findings suggest that the EMG-assisted mode combined with minimise joint moments error and peak HJCF calibration is preferable for the estimation of HJCF and generation of realistic load distribution across muscles.

Calibration of neuromusculoskeletal models using functional tasks is performed to calculate subject-specific musculotendon parameters, as well as coefficients describing the shape of muscle excitation and activation functions. The objective of the present study was to employ a neuromusculoskeletal model of the shoulder driven entirely from muscle electromyography (EMG) to quantify the influence of different model calibration strategies on muscle and joint force predictions. Three healthy adults performed dynamic shoulder abduction and flexion, followed by calibration tasks that included reaching, head touching as well as active and passive abduction, flexion and axial rotation, and submaximal isometric abduction, flexion and axial rotation contractions. EMG data were simultaneously measured from 16 shoulder muscles using surface and intramuscular electrodes, and joint motion evaluated using video motion analysis. Muscle and joint forces were calculated using subject-specific EMG-driven neuromusculoskeletal models that were uncalibrated and calibrated using (i) all calibration tasks (ii) sagittal plane calibration tasks, and (iii) scapular plane calibration tasks. Joint forces were compared to published instrumented implant data. Calibrating models across all tasks resulted in glenohumeral joint force magnitudes that were more similar to instrumented implant data than those derived from any other model calibration strategy. Muscles that generated greater torque were more sensitive to calibration than those that contributed less. This study demonstrates that extensive model calibration over a broad range of contrasting tasks produces the most accurate and physiologically relevant musculotendon and EMG-to-activation parameters. This study will assist in development and deployment of subject-specific neuromusculoskeletal models.

Neuro-musculoskeletal modelling can provide insight into the aberrant muscle function during walking in those suffering cerebral palsy (CP). However, such modelling employs optimization to estimate muscle activation that may not account for disturbed motor control and muscle weakness in CP. This study evaluated different forms of neuro-musculoskeletal model personalization and optimization to estimate musculotendon forces during gait of nine children with CP (GMFCS I-II) and nine typically developing (TD) children. Data collection included 3D-kinematics, ground reaction forces, and electromyography (EMG) of eight lower limb muscles. Four different optimization methods estimated muscle activation and musculotendon forces of a scaled-generic musculoskeletal model for each child walking, i.e. (i) static optimization that minimized summed-excitation squared; (ii) static optimization with maximum isometric muscle forces scaled to body mass; (iii) an EMG-assisted approach using optimization to minimize summed-excitation squared while reducing tracking errors of experimental EMG-linear envelopes and joint moments; and (iv) EMG-assisted with musculotendon model parameters first personalized by calibration. Both static optimization approaches showed a relatively low model performance compared to EMG envelopes. EMG-assisted approaches performed much better, especially in CP, with only a minor mismatch in joint moments. Calibration did not affect model performance significantly, however it did affect musculotendon forces, especially in CP. A model more consistent with experimental measures is more likely to yield more physiologically representative results. Therefore, this study highlights the importance of calibrated EMG-assisted modelling when estimating musculotendon forces in TD children and even more so in children with CP.

Static optimization is commonly employed in musculoskeletal modeling to estimate muscle and joint loading; however, the ability of this approach to predict antagonist muscle activity at the shoulder is poorly understood. Antagonist muscles, which contribute negatively to a net joint moment, are known to be important for maintaining glenohumeral joint stability. This study aimed to compare muscle and joint force predictions from a subject-specific neuromusculoskeletal model of the shoulder driven entirely by measured muscle electromyography (EMG) data with those from a musculoskeletal model employing static optimization. Four healthy adults performed six sub-maximal upper-limb contractions including shoulder abduction, adduction, flexion, extension, internal rotation and external rotation. EMG data were simultaneously measured from 16 shoulder muscles using surface and intramuscular electrodes, and joint motion evaluated using video motion analysis. Muscle and joint forces were calculated using both a calibrated EMG-driven neuromusculoskeletal modeling framework, and musculoskeletal model simulations that employed static optimization. The EMG-driven model predicted antagonistic muscle function for pectoralis major, latissimus dorsi and teres major during abduction and flexion; supraspinatus during adduction; middle deltoid during extension; and subscapularis, pectoralis major and latissimus dorsi during external rotation. In contrast, static optimization neural solutions showed little or no recruitment of these muscles, and preferentially activated agonistic prime movers with large moment arms. As a consequence, glenohumeral joint force calculations varied substantially between models. The findings suggest that static optimization may under-estimate the activity of muscle antagonists, and therefore, their contribution to glenohumeral joint stability.